



'''simple docstring'''

import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__     :						Any	=  [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm_flax
			import run_flax_glue
			import run_flax_ner
			import run_mlm_flax
			import run_qa
			import run_summarization_flax
			import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.getLogger()






def 				a  (     )       ->	Optional[int]:
		snake_case__	     =argparse.ArgumentParser()
		parser.add_argument('-f'			)
		snake_case__	     =parser.parse_args()
		return args.f






def 				a  (     UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    int="eval"			)       ->	Any:
		snake_case__	     =os.path.join(UpperCamelCase_      ,					f"""{split}_results.json"""			)
		if os.path.exists(UpperCamelCase_			):
				with open(UpperCamelCase_      ,					'r'			) as f:
						return json.load(UpperCamelCase_			)
		raise ValueError(f"""can't find {path}"""			)


SCREAMING_SNAKE_CASE__     :						List[Any]	=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 							a__( snake_case__     ):






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

					with patch.object(_UpperCAmelCase					,   'argv'					,   _UpperCAmelCase						):
							run_flax_glue.main()
							snake_case__	     =get_results(_UpperCAmelCase						)
							self.assertGreaterEqual(result['eval_accuracy']					,   0.75						)






			@slow
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

					with patch.object(_UpperCAmelCase					,   'argv'					,   _UpperCAmelCase						):
							run_clm_flax.main()
							snake_case__	     =get_results(_UpperCAmelCase						)
							self.assertLess(result['eval_perplexity']					,   100						)






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

					with patch.object(_UpperCAmelCase					,   'argv'					,   _UpperCAmelCase						):
							run_summarization_flax.main()
							snake_case__	     =get_results(_UpperCAmelCase					,   split='test'						)
							self.assertGreaterEqual(result['test_rouge1']					,   10						)
							self.assertGreaterEqual(result['test_rouge2']					,   2						)
							self.assertGreaterEqual(result['test_rougeL']					,   7						)
							self.assertGreaterEqual(result['test_rougeLsum']					,   7						)






			@slow
			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

					with patch.object(_UpperCAmelCase					,   'argv'					,   _UpperCAmelCase						):
							run_mlm_flax.main()
							snake_case__	     =get_results(_UpperCAmelCase						)
							self.assertLess(result['eval_perplexity']					,   42						)






			@slow
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

					with patch.object(_UpperCAmelCase					,   'argv'					,   _UpperCAmelCase						):
							run_ta_mlm_flax.main()
							snake_case__	     =get_results(_UpperCAmelCase						)
							self.assertGreaterEqual(result['eval_accuracy']					,   0.42						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[Any]:
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					snake_case__	     =7 if get_gpu_count() > 1 else 2

					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

					with patch.object(_UpperCAmelCase					,   'argv'					,   _UpperCAmelCase						):
							run_flax_ner.main()
							snake_case__	     =get_results(_UpperCAmelCase						)
							self.assertGreaterEqual(result['eval_accuracy']					,   0.75						)
							self.assertGreaterEqual(result['eval_f1']					,   0.3						)






			@slow
			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

					with patch.object(_UpperCAmelCase					,   'argv'					,   _UpperCAmelCase						):
							run_qa.main()
							snake_case__	     =get_results(_UpperCAmelCase						)
							self.assertGreaterEqual(result['eval_f1']					,   30						)
							self.assertGreaterEqual(result['eval_exact']					,   30						)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




